Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
You can take IBAP-ML Grundlagen des Maschinellen Lernens in German or IBAP-ML Fundamentals of Machine Learning in English. The course content is the same and can only be credited once.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
The lecture will take place at DFKI.

Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robots to operate in the environment autonomously. The course introduces a basic understanding of robotics, along with tools and methods to control mobile robotic platforms and manipulators. Firstly, the course presents the basics of modeling robotic systems in terms of geometry, kinematics, and dynamics. Next, real robotic systems are considered with their different types of sensors and actuators. Furthermore, system identification as a means to adapt the robot model to the reality is treated. Finally, the course provides methods and approaches to control robots from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• System Identification: methods to identify geometry, kinematic and dyanmic parameters of a robot.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A*).
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Have a basic understanding about autonomous robots and AI.
• Compute the coordinate transformations for rigid bodies commonly used in robotics.
• Apply the robot forward and inverse kinematics.
• Describe a robotic system based on its kinematic and dynamic properties.
• Implement and understand the low-level actuator control methods.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Apply system identification methods to improve robot models and adapt them to reality.
• Use probabilistic methods for robot localization.
• Generate a path for a mobile robot or manipulator using motion planning methods.
• Apply dynamical control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
You can take IBAP-ML Grundlagen des Maschinellen Lernens in German or IBAP-ML Fundamentals of Machine Learning in English. The course content is the same and can only be credited once.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
The lecture will take place at DFKI.

Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robots to operate in the environment autonomously. The course introduces a basic understanding of robotics, along with tools and methods to control mobile robotic platforms and manipulators. Firstly, the course presents the basics of modeling robotic systems in terms of geometry, kinematics, and dynamics. Next, real robotic systems are considered with their different types of sensors and actuators. Furthermore, system identification as a means to adapt the robot model to the reality is treated. Finally, the course provides methods and approaches to control robots from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• System Identification: methods to identify geometry, kinematic and dyanmic parameters of a robot.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A*).
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Have a basic understanding about autonomous robots and AI.
• Compute the coordinate transformations for rigid bodies commonly used in robotics.
• Apply the robot forward and inverse kinematics.
• Describe a robotic system based on its kinematic and dynamic properties.
• Implement and understand the low-level actuator control methods.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Apply system identification methods to improve robot models and adapt them to reality.
• Use probabilistic methods for robot localization.
• Generate a path for a mobile robot or manipulator using motion planning methods.
• Apply dynamical control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
You can take IBAP-ML Grundlagen des Maschinellen Lernens in German or IBAP-ML Fundamentals of Machine Learning in English. The course content is the same and can only be credited once.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
The lecture will take place at DFKI.

Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robots to operate in the environment autonomously. The course introduces a basic understanding of robotics, along with tools and methods to control mobile robotic platforms and manipulators. Firstly, the course presents the basics of modeling robotic systems in terms of geometry, kinematics, and dynamics. Next, real robotic systems are considered with their different types of sensors and actuators. Furthermore, system identification as a means to adapt the robot model to the reality is treated. Finally, the course provides methods and approaches to control robots from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• System Identification: methods to identify geometry, kinematic and dyanmic parameters of a robot.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A*).
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Have a basic understanding about autonomous robots and AI.
• Compute the coordinate transformations for rigid bodies commonly used in robotics.
• Apply the robot forward and inverse kinematics.
• Describe a robotic system based on its kinematic and dynamic properties.
• Implement and understand the low-level actuator control methods.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Apply system identification methods to improve robot models and adapt them to reality.
• Use probabilistic methods for robot localization.
• Generate a path for a mobile robot or manipulator using motion planning methods.
• Apply dynamical control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

Die Veranstaltung findet im TAB in Raum 0.30 statt.
https://lvb.informatik.uni-bremen.de/ibv/03-ibvp-akr.pdf

This course deals with the idea of bringing knowledge into applications to support users in daily life. It therefore covers topics on how knowledge can be represented to be machine-understandable, how knowledge can be acquired from different sources (including Web scraping) and how such different knowledge chunks can be linked. It will further discuss how to reason about knowledge and how different agents like websites, AR applications or robots can use knowledge to support users in their daily life. All exercises will be available in platform-independent jupyter notebooks based on python and have low software requirements.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
You can take IBAP-ML Grundlagen des Maschinellen Lernens in German or IBAP-ML Fundamentals of Machine Learning in English. The course content is the same and can only be credited once.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
The lecture will take place at DFKI.

Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robots to operate in the environment autonomously. The course introduces a basic understanding of robotics, along with tools and methods to control mobile robotic platforms and manipulators. Firstly, the course presents the basics of modeling robotic systems in terms of geometry, kinematics, and dynamics. Next, real robotic systems are considered with their different types of sensors and actuators. Furthermore, system identification as a means to adapt the robot model to the reality is treated. Finally, the course provides methods and approaches to control robots from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• System Identification: methods to identify geometry, kinematic and dyanmic parameters of a robot.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A*).
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Have a basic understanding about autonomous robots and AI.
• Compute the coordinate transformations for rigid bodies commonly used in robotics.
• Apply the robot forward and inverse kinematics.
• Describe a robotic system based on its kinematic and dynamic properties.
• Implement and understand the low-level actuator control methods.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Apply system identification methods to improve robot models and adapt them to reality.
• Use probabilistic methods for robot localization.
• Generate a path for a mobile robot or manipulator using motion planning methods.
• Apply dynamical control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

Die Veranstaltung findet im TAB in Raum 0.30 statt.
https://lvb.informatik.uni-bremen.de/ibv/03-ibvp-akr.pdf

This course deals with the idea of bringing knowledge into applications to support users in daily life. It therefore covers topics on how knowledge can be represented to be machine-understandable, how knowledge can be acquired from different sources (including Web scraping) and how such different knowledge chunks can be linked. It will further discuss how to reason about knowledge and how different agents like websites, AR applications or robots can use knowledge to support users in their daily life. All exercises will be available in platform-independent jupyter notebooks based on python and have low software requirements.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
You can take IBAP-ML Grundlagen des Maschinellen Lernens in German or IBAP-ML Fundamentals of Machine Learning in English. The course content is the same and can only be credited once.

3 SWS Seminar
Raum im KKSB Linz 4
Homepage des KKSB und Uni-Lageplan

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
The lecture will take place at DFKI.

Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robots to operate in the environment autonomously. The course introduces a basic understanding of robotics, along with tools and methods to control mobile robotic platforms and manipulators. Firstly, the course presents the basics of modeling robotic systems in terms of geometry, kinematics, and dynamics. Next, real robotic systems are considered with their different types of sensors and actuators. Furthermore, system identification as a means to adapt the robot model to the reality is treated. Finally, the course provides methods and approaches to control robots from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• System Identification: methods to identify geometry, kinematic and dyanmic parameters of a robot.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A*).
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Have a basic understanding about autonomous robots and AI.
• Compute the coordinate transformations for rigid bodies commonly used in robotics.
• Apply the robot forward and inverse kinematics.
• Describe a robotic system based on its kinematic and dynamic properties.
• Implement and understand the low-level actuator control methods.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Apply system identification methods to improve robot models and adapt them to reality.
• Use probabilistic methods for robot localization.
• Generate a path for a mobile robot or manipulator using motion planning methods.
• Apply dynamical control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

https://lvb.informatik.uni-bremen.de/igs/03-ibfw-hto.pdf
A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be formulated as discrete linear optimization problems. This course briefly introduces the theory of such problems. We develop a toolkit to model real-world problems as (discrete) linear programs. We also explore several ways to find integer solutions such as cutting planes, branch & bound, and column generation.

Throughout the course, we learn these skills by modeling and solving, for example, scheduling, packing, matching, routing, and network-design problems. We focus on translating practical examples into mixed-integer linear programs. We learn how to use solvers (such as CPLEX, Gurobi, Xpress and free ones) and tailor the solution process to certain properties of the problem.

This course consists of two phases:

• One week Mon-Fri (full day, 9-5) of lectures and practical labs: July 14-18, in MZH.
• A subsequent project period: One problem has to be modeled, implemented, and solved individually or in a group of at most three students. The topic will be provided by the lecturers and will be discussed on the last day of the block course. The project including the implementation has to be presented in the beginning of the winter semester.

There are no prerequisites except some basic programming skills to participate.